Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy -medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distance between the parameters estimated by an INGARCH model. We show how the proposed clustering method works by clustering football teams according to the number of scored goals.
INGARCH-based fuzzy clustering of count time series with a football application / Cerqueti, Roy; D'Urso, Pierpaolo; De Giovanni, Livia; Mattera, Raffaele; Vitale, Vincenzina. - In: MACHINE LEARNING WITH APPLICATIONS. - ISSN 2666-8270. - (2022).
INGARCH-based fuzzy clustering of count time series with a football application
Roy Cerqueti;Pierpaolo D’Urso;Raffaele Mattera;Vincenzina Vitale.
2022
Abstract
Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy -medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distance between the parameters estimated by an INGARCH model. We show how the proposed clustering method works by clustering football teams according to the number of scored goals.File | Dimensione | Formato | |
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